A Hybrid Tabu Scatter Search Algorithm for Simulation-Based Optimization of Multi-Objective Runway Operations Scheduling

TL;DR

This paper introduces a hybrid optimization algorithm combining Tabu Search and Scatter Search within a simulation framework to effectively solve multi-objective runway scheduling problems under uncertainty, improving efficiency and fairness.

Contribution

It presents a novel hybrid SbO approach that explicitly models uncertainty and fairness, outperforming traditional methods in multi-objective runway operations scheduling.

Findings

Outperforms traditional scheduling methods like FCFS.

Effectively handles stochastic conditions with simulation-based optimization.

Generates Pareto-optimal trade-off solutions for multiple objectives.

Abstract

This dissertation addresses the growing challenge of air traffic flow management by proposing a simulation-based optimization (SbO) approach for multi-objective runway operations scheduling. The goal is to optimize airport capacity utilization while minimizing delays, fuel consumption, and environmental impacts. Given the NP-Hard complexity of the problem, traditional analytical methods often rely on oversimplifications and fail to account for real-world uncertainties, limiting their practical applicability. The proposed SbO framework integrates a discrete-event simulation model to handle stochastic conditions and a hybrid Tabu-Scatter Search algorithm to identify Pareto-optimal solutions, explicitly incorporating uncertainty and fairness among aircraft as key objectives. Computational experiments using real-world data from a major U.S. airport demonstrate the approach's effectiveness and tractability, outperforming traditional methods such as First-Come-First-Served (FCFS) and deterministic approaches while maintaining schedule fairness. The algorithm's ability to generate trade-off solutions between competing objectives makes it a promising decision support tool for air traffic controllers managing complex runway operations.